Real-Time Scheduling Approaches for Vehicle-Based Internal Transport Systems

In this paper, we study the problem of scheduling and dispatching vehicles in vehicle-based internal transport systems within warehouses and production facilities. We develop and use two rolling horizon policies to solve real-time vehicle scheduling problems. To solve static instances of scheduling problems, we propose two new heuristics: combined and column-generation heuristics. We solve a real-time scheduling problem by applying a heuristic to dynamically solve a series of static instances under a rolling horizon policy. A rolling horizon can be seen either as a fixed-time interval in which advance information about loads’ arrivals is available, or as a fixed number of loads which are known to become available in the near future. We also propose a new look-ahead dynamic assignment algorithm, a different dynamic vehicle-scheduling approach. We evaluate these dynamic scheduling strategies by comparing their performance with that of two of the best online vehicle dispatching rules mentioned in the literature. Experimental results show that the new look-ahead dynamic assignment algorithm and dynamic scheduling approaches consistently outperform vehicle dispatching rules

Online Dispatching Rules For Vehicle-Based Internal Transport Systems

On-line vehicles dispatching rules are widely used in many facilities such as warehouses to control vehicles' movements. Single-attribute dispatching rules, which dispatch vehicles based on only one parameter, are used commonly. However, multi-attribute dispatching rules prove to be better in general. In this study, we introduce new dispatching rules and evaluate their performance compared to several good dispatching rules in literature, using the experimental design of a real case study. The performance criteria are minimizing the average load waiting time while keeping the maximum load waiting time as small as possible and better utilize vehicles. The experiments show that newly introduced hybrid dispatching rule yields the best performance overall

A Review Of Design And Control Of Automated Guided Vehicle Systems

This paper presents a review on design and control of automated guided vehicle systems. We address most key related issues including guide-path design, estimating the number of vehicles, vehicle scheduling, idle-vehicle positioning, battery management, vehicle routing, and conflict resolution. We discuss and classify important models and results from key publications in literature on automated guided vehicle systems, including often-neglected areas, such as idle-vehicle positioning and battery management. In addition, we propose a decision framework for design and implementation of automated guided vehicle systems, and suggest some fruitful research directions

Multi-Attribute Dispatching Rules For Agv Systems With Many Vehicles

Internal transport systems using automated guided vehicles (AGVs) are widely used in many facilities such as warehouses, distribution centers and transshipment terminals. Most AGV systems use online dispatching rules to control vehicle movements. In literature, there are many types of dispatching rules such as single- and multi-attribute dispatching rules. However, a dispatching rule that is good for all cases does not exist. In this research, we study a specific type of AGV environments which has not received much attention from researchers - AGV systems with many vehicles as can be seen in airport baggage handling systems. We propose two new multi-attribute dispatching rules for this type of environment and compare their performance with that of two of the best dispatching rules in literature. Using simulation we show that the new multi-attribute dispatching rules are robust and perform very well

Intelligent Control of Vehicle-Based Internal Transport Systems

“Intelligent control of vehicle-based internal transport (VBIT) systems” copes with real-time dispatching and scheduling of internal-transport vehicles, such as forklifts and guided vehicles. VBIT systems can be found in warehouses, distribution centers, manufacturing plants, airport and transshipment terminals. Using simulation of two realworld environments, dispatching rules described in literature and several newly introduced rules are compared on performance. The performance evaluation suggests that in environments where queue space is not a restriction, distance-based dispatching rules such as shortest-travel-distance-first outperform time-based dispatching rules such as modified-first-come-first-served and using load prearrival information has a significant positive impact on reducing the average load waiting time. Experimental results also reveal that multi-attribute dispatching rules combining distance and time aspects of vehicles and loads are robust to variations in working conditions. In addition, multi-attribute rules which take vehicle empty travel distance and vehicle requirement at a station into account perform very well in heavy-traffic VBIT systems such as baggage handling systems. Besides dispatching rules, the potential contribution of dynamic vehicle scheduling for VBIT systems is investigated. Experiments using simulation in combination with optimization show that when sufficient pre-arrival information is available a dynamic scheduling approach outperforms the dispatching approach. This thesis also evaluates the impact of guide-path layout, load arrival rate and variance, and the amount of load pre-arrival information on different vehicle control approaches (scheduling and dispatching). Based on experimental results, recommendations for selecting appropriate vehicle control approaches for specific situations are presented

Conditional Support Alignment for Domain Adaptation with Label Shift

Unsupervised domain adaptation (UDA) refers to a domain adaptation framework in which a learning model is trained based on the labeled samples on the source domain and unlabelled ones in the target domain. The dominant existing methods in the field that rely on the classical covariate shift assumption to learn domain-invariant feature representation have yielded suboptimal performance under the label distribution shift between source and target domains. In this paper, we propose a novel conditional adversarial support alignment (CASA) whose aim is to minimize the conditional symmetric support divergence between the source's and target domain's feature representation distributions, aiming at a more helpful representation for the classification task. We also introduce a novel theoretical target risk bound, which justifies the merits of aligning the supports of conditional feature distributions compared to the existing marginal support alignment approach in the UDA settings. We then provide a complete training process for learning in which the objective optimization functions are precisely based on the proposed target risk bound. Our empirical results demonstrate that CASA outperforms other state-of-the-art methods on different UDA benchmark tasks under label shift conditions